Nick Edwards

in the next 3 years, every major AI lab will spin up its own bio arm and in-house wet labs. biology is the next big bet in AI after code pay attention.

**the emerging AI native life science R&D stack** the key question from mid 2025 til recently was whether frontier labs were actually serious about building products, capabilities, and orgs in AI x drug discovery or they were using it for marketing purposes in pursuit of ever larger rounds of funding. fair q when in a few week stretch in 2025, sam altman, demis,and dario all said that one of the biggest benefits of AI for humanity would be huge acceleration of tx development ("dozens of drugs in a decade!") - cue exasperated groans from the trad bio section of the peanut gallery a few cards have flipped in last few weeks: OAI: released GPT-rosalind, a life science research model, first vertical specific GPT Anthropic: acquired Coefficient bio to build biotech infra and a rumored bio model also dropping soon as the frontier labs' strategy in the space has become clearer, so too has the *AI-native life science R&D stack* a few comments on each layer of this 5 layer cake, starting with the middle: Intelligence Layer (Frontier + Specialized Models) ~ Ant, OAI, GDP all in running; will proprietary data end up being *the* differentiator? and if so, who actually has access? Wet Lab Coordination ~ speaking of proprietary data, can't get it at scale without some interface layer to the actual wet lab execution apparatus. in life sciences, that workflow is super outdated, phone calls, Excel, fax , PDFs, all just archaic. nearly no one has an API. are the frontier labs interested in tackling the long tail of assays and CROs that would need to be wired into a real wet lab coordination layer? nothing to suggest they will right now — but they are hungry for capturing value up and down the chain AWS Bio is first green shoots that another player will operate in this space but reviews on the ground have not been great - this may be the grittiest but also most unappreciated oppty in the stack Wet Lab Execution ~ life sciences has a massive long tail of CROs, and given this is where the actual proprietary data gets generated, so this layer can be a genuinely differentiating factor the interesting topic to watch: are any CROs going to become AI-native and start moving *up* the stack — doing wet lab coordination themselves, or perhaps even becoming preferred data providers to frontier labs? Early movers like Gingko and Adaptyv are making some noise, but this has to be a topic that the forward-thinking folks running AI strategy at Thermo, Wuxi, and others are thinking about Agents / Harness Layer ~ sitting on top of intelligence layer, lots of new startups have jumped into this space trying to coordinate models across life sciences specific workflows big risk looming over all of them is whether frontier labs will simply subsume this into their own product roadmap. Anthropic x Coefficient Bio is an ominous signal (but maybe $ 400M acqui-hire in 12 mo is an outcome that everyone involved is ok with) Application Layer ~ Benchling is the big gorilla here but if "attention is all you need" is *truly* all you need, the UX / UI with scientist layer becomes critically important, and potentially the most interesting place for a shake-up frontier labs could still move in. and new form factors could emerge enabling new startups. physical AI could change the whole workflow additionally is a notebook entry in a digital ELN even the right atomic unit of work in an AI-native workflow? finally, stepping outside the stack, the looming question that no one has fully answered yet - these are all *infrastructure* plays. what will the truly AI-native therapeutics company actually look like? the actual value creation that comes out of this stack? how will those AI-native biotechs look different in shape, value creation profile, and capital intensity compared to the biotechs we know today? stay tuned.